Beycimen, SemihIgnatyev, DmitryZolotas, Argyrios2022-12-132022-12-132022-10-13Beycimen S, Ignatyev D, Zolotas A. (2022) Predicting autonomous vehicle navigation parameters via image and image-and-point cloud fusion-based end-to-end methods. In: 2022 IEEE International Conference on Multisensor Fusion and Integration (MFI 2022), 20-22 September 2022, Cranfield, UK978-1-6654-6027-9https://doi.org/10.1109/MFI55806.2022.9913844https://dspace.lib.cranfield.ac.uk/handle/1826/18794This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. Image-based and Image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained Image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Conference paper978-1-6654-6026-2